Precipitation Accumulation Research Articles

Verification of the Global Forecast System, North American Mesoscale Forecast System, and High-Resolution Rapid Refresh Model Near-Surface Forecasts by Use of the New York State Mesonet

Abstract Global Forecast System (GFS), North American Mesoscale Forecast System (NAM), and High-Resolution Rapid Refresh (HRRR) 2-m temperature, 10-m wind speed, and precipitation accumulation forecasts initialized at 1200 UTC are verified against New York State Mesonet (NYSM) observations from 1 January 2018 through 31 December 2021. NYSM observations at 126 site locations are used to calculate standard error statistics (e.g., forecast error, root-mean-square error) for temperature and wind speed and contingency table statistics for precipitation across forecast hours, meteorological seasons, and regions. The majority of the focus is placed on the first 18 forecast hours to allow for comparison among all three models. A daily NYSM station-mean temperature error analysis identified a slight cold bias at temperatures below 25°C in the GFS, a cool-to-warm bias as forecast temperatures warm in the HRRR, and a warm bias at temperatures above 30°C in each model. Differences arise when considering temperature biases with respect to lead times and seasons. Wind speeds are overforecast at all ranges in each season, and forecast wind speeds ≥ 18 m s−1 are rarely observed. Performance diagrams indicate overall good forecast performance at precipitation thresholds of 0.1–1.5 mm, but with a high frequency bias in the GFS and NAM. This paper provides an overview of deterministic forecast performance across New York State, with the aim of sharing common biases associated with temperature, wind speed, and precipitation with operational forecasters and is the first step in developing a real-time model forecast uncertainty prediction tool.

Atmospheric CO2 Sequestration in Seawater Enhanced by Molluscan Shell Powders.

Carbon capture, utilization, and storage (CCUS) are widely recognized as a promising technology for mitigating climate change. CO2 mineralization using Ca-rich fluids and high-concentration CO2 gas has been studied extensively. However, few studies have reported CO2 mineralization with atmospheric CO2, owing to the difficulty associated with its low concentration. In seawater, the biomineralization process promotes Ca accumulation and CaCO3 precipitation, assisted by specific organic matter. In this study, we examined the conversion of atmospheric CO2 into CaCO3 in seawater using shell powders (Pinctada fucata, Haliotis discus, Crassostrea gigas, Mizuhopecten yessoensis, Turbo sazae, and Saxidomus purpurata). Among the six species, the shell powder of S. purpurata showed the highest rate of CaCO3 formation and recovery of CaCO3. NaClO treatment test revealed that the organic matter in the shells enhanced the CO2 mineralization. All materials used in this study, including atmospheric CO2, seawater, and shells, are economically feasible for large-scale applications. Using shell powder for CO2 mineralization in seawater embodies an innovative technological advancement to address climate change.

Statistical characteristics of convective and stratiform precipitation during the rainy season over South China based on GPM-DPR observations

South China is one of the wettest regions in the world, and is prone to precipitation-related disasters. The GPM-DPR product is utilized in this study to investigate the vertical structure and drop size distribution (DSD) characteristics of different precipitation types, i.e., stratiform precipitation (SP) and convective precipitation (CP) in South China. The precipitation frequency and accumulation in South China are dominated by SP and deep CP, respectively. Shallow CP has comparable occurrence ratio to deep CP, but the mean intensity is much lower. As such, it contributes <5% to the total rainfall. Vertical features differ among precipitation types and intensities. Higher storm top altitude (STA) is roughly related to more intense precipitation, especially for CPs. As surface PR increases in deep CP, the contribution of increase of PR (ΔPR) in liquid-phase region to total PR also increases, from approximately 10% for 4 mm/h to 55% for 100 mm/h. The intensity of shallow CP is generally low, but ΔPR in the liquid-phase region is significant. For SP and shallow CP, higher precipitation intensity is often caused by higher hydrometer concentration. Differently, for deep CP with PR >15 mm/h, both the increases in hydrometer size and concentration contribute to the increase in PR. The collision-coalescence process is dominant in the low-level layers in SP without bright band and CP, and approximately four fifth of shallow CP samples are influenced by collision-coalescence process. Compared with inland mountainous region, collision-coalescence process plays more important role in intense precipitation in coastal region. The results of this study indicate that it is important to consider the vertical evolution and DSD characteristics of different precipitation types for improving the accuracy of near surface quantitative precipitation estimate.

Interannual variation in lake areas over 50 km² on the Tibetan Plateau from 1986 to 2020 based on remote sensing big data

ABSTRACT Lake distribution on the Tibetan Plateau (TP) is extensive, and lake area changes are key indicators of the TP's climate change response. Many multisource remote sensing big data for the TP, particularly optical images, are unusable due to cloud cover. Therefore, an improved isobath interpolation-based lake area extraction method is proposed and applied to obtain annual average lake areas (≥ 50 km²) on the TP from 1986 to 2020 using remote sensing big data. The lake area result accuracy was verified using existing lake area and level datasets, yielding correlation coefficients of ∼0.9. The change points and segmented trends of each lake's interannual area sequence were obtained. The relationships between lake area and climatic variables were investigated. The positive accumulation of the total precipitation minus total evaporation explains the overall lake area expansion trend after 1995. The exorheic lake interannual area is related to precipitation more than that of endorheic lakes, but endorheic lake area changes are stronger. The shrinking of lakes on the southern TP may not be climate-driven but probably attributed to lake bottom leakage. We explore detailed interannual variation characteristics of lake areas on the TP and provide reference data for studying lake responses to climate change.

Analysis of the potential of the city of Pokrovsk for the accumulation and use of atmospheric precipitation

The problem of water shortage in the city of Pokrovsk, Donetsk region, requires the search for alternative sources of water supply, especially for the needs of industry and agriculture. Atmospheric precipitation is considered as an alternative source. However, at the stage of choosing and developing a method of managing rainwater from an urbanized area and implementing technical means for its accumulation, storage, and use, it is important to assess the quantitative indicators of stormwater runoff. Research is aimed at substantiating the possibility of using atmospheric precipitation as an auxiliary source of water supply for industrial-communal, domestic, and agricultural needs based on statistical determination of the dynamics of precipitation on the territory of the city of Pokrovsk. Changes in rainwater runoff parameters were determined based on the analysis of the meteorological data archive for the period 1985–2022.The study found that the city of Pokrovsk has the potential for wastewater accumulation. The city has a tendency to increase the frequency of high-intensity storms with a 34.89% increase in the average height of the precipitation layer for one abnormal rainy day. Over the period 2004–2022, the average annual and annual rainfall per rainy day increased slightly by 0.56% and 6.23%, respectively, compared to the same period in the previous year (1985–2003), while the average number of rainy days per year decreased by 5.25%.The dynamics of annual volumes of surface wastewater indicates the need to improve the city's rainwater accumulation system, which should provide a 35% increase in the current flow rate of abnormal rainfall and eliminate the risk of flooding. To do this, the storage capacity should be increased by deepening the city pond "Nulyovka" and the ridge located downstream. It is expedient for private households to create individual sediment accumulators.

Assessment of the abiotic and anthropogenic factors influence on soybean nutrition and yield

The research was carried out in a long-term stationary experiment with mineral fertilizers, laid in 1962-1964 on meadow chernozem-like soil in Sadovoye village of the Tambov district of the Amur region, in 2020-2022. The object of the study was the crops of the precocious soybean variety Sentyabrinka, which is the fourth crop of the five-field crop rotation. The aim of the research was to study the influence of external factors on the intake and content of nutrients, productivity and biological yield of soybean seeds in the system of long-term use of fertilizers. Of the abiotic factors, the accumulation of heat and precipitation had a positive effect on the growth and development of soybean plants of the September variety, the intake of mineral nutrition elements from the soil. The most favorable conditions for the formation of a high yield were formed in 2021, due to sufficient precipitation in July (104 mm) and August (194 mm), which contributed to the active formation of beans and seeds in early-ripening plants. The anthropogenic factor of fertilization had a positive effect on the number and weight of seeds from one plant. The average annual loads of N24P30K24, N42P48 and N24P30 + manure 4.8 t/ha provide an increase in the number of seeds by 3.6-3.7 compared to the control, and the weight by 2.3-3.4%, depending on the average annual load of fertilizers. At the same time, the mass of 1000 seeds was greater than in the control by 0.9-4.8 g. The positive effect of fertilizers on plant nutrition led to an increase in the biological yield of seeds, which was the highest in the weather conditions of 2020 and 2021. On average, over 3 years of research, there was a stable tendency to increase biological yield against the background of the action and aftereffect of fertilizers by 0.23-0.68 t/ha with coefficients varying by years from 2.1 to 10.4%, depending on the average annual load of fertilizers.

Minimization of residues generated during the operation of treatment facilities

The activity of sewage treatment plants for the treatment of domestic wastewater is not limited only to wastewater treatment. The main problem in this activity is the treatment and disposal of the resulting sediments at sewage treatment plants, where the main task is to reduce the volume of sediment and then transform it into a useful product that does not cause environmental pollution. Currently, representatives of landfills refuse to accept this type of precipitation, explaining that this type of precipitation does not belong to the category of solid municipal waste. The issue of using sediments from sewage treatment plants as raw materials for manufacturing remains open, but in our opinion this type of sediment can be recycled in wastewater treatment technology. The practical value lies in conducting industrial tests of the converter of sewage sludge into liquid effluents, including all waste generated during the operation of sewage treatment plants with subsequent post-treatment of liquid effluents at wastewater treatment plants. These tests will reduce the amount of sediment by up to 90%, which will minimize the intensive accumulation of precipitation from sewage treatment plants and improve the state of the environment.

From heavy rain removal to detail restoration: A faster and better network

The profound accumulation of precipitation during intense rainfall events can markedly degrade the quality of images, leading to the erosion of textural details. Despite the improvements observed in existing learning-based methods specialized for heavy rain removal, it is discerned that a significant proportion of these methods tend to overlook the precise reconstruction of the intricate details. In this work, we introduce a simple dual-stage progressive enhancement network, denoted as DPENet, aiming to achieve effective deraining while preserving the structural accuracy of rain-free images. This approach comprises two key modules, a rain streaks removal network (R2Net) focusing on accurate rain removal, and a details reconstruction network (DRNet) designed to recover the textural details of rain-free images. Firstly, we introduce a dilated dense residual block (DDRB) within R2Net, enabling the aggregation of high-level and low-level features. Secondly, an enhanced residual pixel-wise attention block (ERPAB) is integrated into DRNet to facilitate the incorporation of contextual information. To further enhance the fidelity of our approach, we employ a comprehensive loss function that accentuates both the marginal and regional accuracy of rain-free images. Extensive experiments conducted on publicly available benchmarks demonstrates the noteworthy efficiency and effectiveness of our proposed DPENet. The source code and pre-trained models are currently available at https://github.com/chdwyb/DPENet.

Multi-scale failure mechanisms of hydraulic engineering exposed to seasonally frozen salinization environment: Integrating SBAS-InSAR and mechanical experiments

Constructing hydraulic engineering ensures agricultural development and improves salinization environments. However, in seasonally frozen salinization regions, hydraulic engineering is prone to deformation failure. Leakage from canal raises the regional groundwater level, triggering secondary salinization environmental issues. Exploring the instability mechanisms is thus necessary for hydraulic engineering. Traditional deformation monitoring techniques and soil experiments are constrained by observation scale and timeliness. In this study, Sentinel-1B data from November 2017 to August 2019 were acquired. The small baseline subset (SBAS) InSAR approach was employed to interpret the seasonal deformation characteristics in both the vertical and slope directions of a damaged canal segment in Songyuan, Northeast China. The mechanical properties of saline-alkali soil under varying water contents were quantified by integrating unconfined compression experiment (UCE). In May, as the soil thawed downward, a frozen lenses with poor permeability formed at a depth of approximately 100 cm, causing the accumulation of meltwater and infiltrated precipitation between the frozen layer and the melting layer in the canal. The soil water content at a depth of 80 to 140 cm exceeded 22 %, reaching a threshold for rapid reduction in unconfined compression strength (UCS). Consequently, in spring, the low soil strength between the frozen layer and the melting layer resulted in interface sliding, with a displacement of −133.88 mm in the canal slope direction. Furthermore, the differential projection of freeze-thaw deformation in the slope direction caused continuous creep of the canal towards the free face, with a value of −23.27 mm, exacerbating the formation of the late spring landslide. Integrating InSAR and engineering geological analysis is beneficial for addressing deformation issues in hydraulic engineering. Ensuring the sustainable operation of hydraulic engineering holds important implications for mitigating the salinization process.

An improved estimate of daily precipitation from the ERA5 reanalysis

AbstractPrecipitation is an essential climate variable and a fundamental part of the global water cycle. Given its importance to society, precipitation is often assessed in climate monitoring activities, such as in those led by the Copernicus Climate Change Service (C3S). To undertake these activities, C3S predominantly uses ERA5 reanalysis precipitation. Research has shown that short‐range forecasts for precipitation made from this reanalysis can provide valuable estimates of the actual (observed) precipitation in extratropical regions but can be less useful in the tropics. While some of these limitations will be reduced with future reanalyses because of the latest advancements, there is potentially a more immediate way to improve the precipitation estimate. This is to use the precipitation modelled in the Four‐Dimensional Variational (4D‐Var) data assimilation window of the reanalysis, and it is the aim of this study to evaluate this approach. Using observed 24‐h precipitation accumulations at 5637 stations from 2001 to 2020, results show that smaller root‐mean‐square errors (RMSEs) and mean absolute errors are generally found by using the ERA5 4D‐Var precipitation. For example, for all available days from 2001 to 2020, 87.5% of stations have smaller RMSEs. These improvements are driven by reduced random errors in the 4D‐Var precipitation because it is better constrained by observations, which are themselves sensitive to or influence precipitation. However, there are regions (e.g., Europe) where larger biases occur, and via the decomposition of the Stable Equitable Error in Probability Space score, this is shown to be because the 4D‐Var precipitation has a wetter bias on ‘dry’ days than the standard ERA5 short‐range forecasts. The findings also highlight that the 4D‐Var precipitation does improve the discrimination of ‘heavy’ observed events. In conclusion, an improved ERA5 precipitation estimate is largely obtainable, and these results could prove useful for C3S activities and for future reanalyses, including ERA6.

Climate and Wolbachia Impacts on Anoplolepis gracilipes (Hymenoptera: Formicidae).

The yellow crazy ant (Anoplolepis gracilipes (Smith, 1857)) is a prominent invasive species exhibiting variable population dynamics. Through collecting long-term climate data and validating field surveys with MaxEnt SDM projections, our results indicated that winter temperature and precipitation accumulation strongly influence the population dynamics. An aggression analysis showed that A. gracilipes nests with higher aggression levels (over 2.5 scores) experienced a higher mean maximum temperature (31.84 ± 0.43 °C) and lower prevalence of wAgra (84.8 ± 4.70%) in A. gracilipes from June to October. The nest manipulation and aggression experiments confirmed that temperature increases aggression (1.3 to 2.8 scores) among A. gracilipes workers due to the reduced prevalence of wAgra. To the best of our knowledge, this is the first case of a notable reduction in the prevalence of Wolbachia (100 to 66%) within a colony of A. gracilipes while maintaining stable nests for further experiments.

Understanding extreme precipitation scaling with temperature: insights from multi-spatiotemporal analysis in South Korea

With global warming, the intensification of extreme precipitation events is anticipated to follow an exponential growth pattern aligned with the Clausius–Clapeyron (CC) scaling rate (approximately 7% per degree Celsius). However, the regional-scale response of extreme precipitation shows significant variability, deviating from the expected CC rate. This deviation is likely caused by diverse weather patterns and local fluctuations in thermodynamic influences, resulting in differences across seasons and within the region of interest. In this study, we examine the spatial distribution of scaling relationships between extreme precipitation and temperature in South Korea, considering daily and sub-daily scales, both annually and seasonally. For a thorough analysis, we utilize multiple precipitation accumulation periods, temperatures, and different conditional quantiles. Our results reveal that, at the annual scale, most scaling patterns exhibit a peak-like structure, with significant variations in breakpoints observed across temperature variables and regions. However, the southern area presents a notable exception with a positive scaling pattern, particularly with the dew point temperature. At the seasonal scale, we observe more variability, with notable shifts occurring during the wet season across different temperatures and regions. Lastly, we explore the long-term historical changes in the peak value in extreme precipitation and find significant increases at high quantiles in the southern area of South Korea. It informs that the observed peak like pattern does not impose a potential upper limit for extreme precipitation. Overall, our findings emphasize the need for cautious interpretation of precipitation scaling within specific spatiotemporal contexts, which could provide a solid basis for better understanding future extreme precipitation events in a changing climate.

Evaluating 3 decades of precipitation in the Upper Colorado River basin from a high-resolution regional climate model

Abstract. Convection-permitting regional climate models (RCMs) have recently become tractable for applications at multi-decadal timescales. These types of models have tremendous utility for water resource studies, but better characterization of precipitation biases is needed, particularly for water-resource-critical mountain regions, where precipitation is highly variable in space, observations are sparse, and the societal water need is great. This study examines 34 years (1987–2020) of RCM precipitation from the Weather Research and Forecasting model (WRF; v3.8.1), using the Climate Forecast System Reanalysis (CFS; CFSv2) initial and lateral boundary conditions and a 1 km × 1 km innermost grid spacing. The RCM is centered over the Upper Colorado River basin, with a focus on the high-elevation, 750 km2 East River watershed (ERW), where a variety of high-impact scientific activities are currently ongoing. Precipitation is compared against point observations (Natural Resources Conservation Service Snow Telemetry or SNOTEL), gridded climate datasets (Newman, Livneh, and PRISM), and Bayesian reconstructions of watershed mean precipitation conditioned on streamflow and high-resolution snow remote-sensing products. We find that the cool-season precipitation percent error between WRF and 23 SNOTEL gauges has a low overall bias (x^ = 0.25 %, s = 13.63 %) and that WRF has a higher percent error during the warm season (x^ = 10.37 %, s = 12.79 %). Warm-season bias manifests as a high number of low-precipitation days, though the low-resolution or SNOTEL gauges limit some of the conclusions that can be drawn. Regional comparisons between WRF precipitation accumulation and three different gridded datasets show differences on the order of ± 20 %, particularly at the highest elevations and in keeping with findings from other studies. We find that WRF agrees slightly better with the Bayesian reconstruction of precipitation in the ERW compared to the gridded precipitation datasets, particularly when changing SNOTEL densities are taken into account. The conclusions are that the RCM reasonably captures orographic precipitation in this region and demonstrates that leveraging additional hydrologic information (streamflow and snow remote-sensing data) improves the ability to characterize biases in RCM precipitation fields. Error characteristics reported in this study are essential for leveraging the RCM model outputs for studies of past and future climates and water resource applications. The methods developed in this study can be applied to other watersheds and model configurations. Hourly 1 km × 1 km precipitation and other meteorological outputs from this dataset are publicly available and suitable for a wide variety of applications.

Unprecedented mass gain over the Antarctic ice sheet between 2021 and 2022 caused by large precipitation anomalies

The Antarctic ice sheet (AIS) is susceptible to global climate change, and its mass loss has been 92 ± 18 Gt yr −1 between 1992 and 2020. Given the current intensive global warming, we investigate the AIS mass changes from January 2003 to December 2022, using the newly released satellite gravimetry and atmospheric datasets. The results show that the continuous mass loss in the AIS between 2003 and 2020 was 141.8 ± 55.6 Gt yr−1 . However, the AIS showed a record-breaking mass gain of 129.7 ± 69.6 Gt yr −1 between 2021 and 2022. During this period, the mass gain over the East AIS and Antarctic Peninsula was unprecedented within the past two decades, and it outpaced the mass loss in the Amundsen sector of the West AIS from 2003 to 2022. Basin-scale analysis shows that the mass gain mainly occurred over Wilhelm II Land, Queen Mary Land, Wilkes Land, and the Antarctic Peninsula due to anomalously enhanced precipitation. Further investigation reveals that during 2021–2022, a pair of symmetrically distributed high-low pressure systems, located at approximately 120°W and 60°E in the Southern Ocean, drove the observed abnormal precipitation and mass accumulation.

Mesoscale and Synoptic Scale Analysis of Narrow Cold Frontal Rainband During a Landfalling Atmospheric River in California During January 2021

AbstractNarrow cold‐frontal rain bands (NCFR) often produce short‐duration and high‐intensity precipitation that can lead to flooding and debris flow in California (CA). On 27 January 2021, an atmospheric river (AR) associated with an intense surface cyclone made landfall over coastal northern CA, which featured a prominent NCFR. This study uses high‐resolution West Weather Research and Forecasting simulations to accurately resolve the gap and core structure of the NCFR and provide reliable precipitation estimations, compensating for limitations of radar and satellite observations. This NCFR was supported by robust synoptic‐scale quasi‐geostrophic (QG) forcing for ascent and frontogenesis. It propagated southward from Cape Mendocino to Big Sur in 12 hr before stalling and rotating counter‐clockwise in central/southern CA due to upstream Rossby wave breaking and an amplifying upper‐tropospheric trough. With the lower to middle tropospheric flow backed considerably to the south‐southwest over the NCFR, the increase of the vertical wind shear caused the transition from parallel to trailing stratiform precipitation. The stall and pivot of the AR and NCFR led to intense rainfall with a 2‐day precipitation accumulation greater than 300 mm over central CA. In addition, under the potential instability and frontogenesis, a moist absolutely unstable layer between 850 and 700 hPa was captured at the leading edge of the NCFR, which indicated slantwise deep layer lifting and high precipitation efficiency. This study reveals synoptic‐scale and mesoscale drivers of rainfall outside orographic lifting and reaffirms the importance of high‐resolution numerical modeling for the prediction of extreme precipitation and related natural hazards.

The influence of deep loosening on the ecological and meliorational state of drained mineral soils

The existing problems of energy, water, and food crises and climate changes determine the need to implement appropriate adaptive measures to improve the ecological and reclamation state of drained mineral soils. For the application of deep loosening of the soil as an adaptive agromelioration measure, a transition from traditional crack loosening of the soil and strip loosening of the soil to improved technologies of continuous layer-by-layer loosening of the soil based on energy-efficient and moisture-regulating principles is proposed. Their comparative evaluation according to the main indicators of overall efficiency was carried out on drained mineral soils of a representative object in the Western Polissia zone of Ukraine. According to the results of the evaluation of various technologies of deep soil loosening, it was determined that the technologies and technical means of continuous deep loosening of the soil have an advantage in all main indicators: agrotechnical, water-physical, energy, technological, ecological, and economic. In particular, the moisture availability of the soil increased by 27 % due to better accumulation of atmospheric precipitation, the yield of cultivated crops also increased by 20-30 %, and the aftereffect period increased to 4 years. This is a cost-effective and investment-friendly adaptive measure to ensure a satisfactory ecological and reclamation state of drained mineral soils (with an investment payback of 1 year). This provides modern principles of adaptive land use in changing climatic conditions and can be an effective alternative to expensive reconstruction and modernization of existing drainage systems.

Which adjustment methods are suitable for the wind-induced errors of Geonor T-200BM3 precipitation weighing gauges in a periglacial site?

Single Alter shielded T-200BM3 weighing precipitation gauges are widely used in the measurement of all precipitation types (rainfall, snow and mixed precipitation) in unattended boreal or alpine regions, but their original datasets must be adjusted for undercatch errors caused by wind in snowy, windy and harsh environments. Therefore, previous researchers have developed many adjustment methods for all precipitation types on different time scales. However, which adjustment method is suitable for T-200BM3 weighing gauge wind-induced error adjustment in harsh alpine regions is unclear. Therefore, precipitation measurement intercomparison experiments were conducted in the Qilian Mountains from July 2018 to July 2021, and eight adjustment methods; were evaluated for wind-induced errors for daily, individual precipitation event, hourly, and half-hourly time scales. Z2004 outperformed the other adjustment methods in regard to the daily measurements of snow and mixed precipitation. Regarding individual snowfall events, M2007 reduced the absolute value of RMSE (bias) from 1.44 to 1.32 mm (0.77–0.24 mm) and could be recommended for snowfall event adjustment. K2017-1 attained a better performance than K2017-2 in regard to half-hourly snowfall and mixed sample adjustment and was more suitable for half-hourly snowfall sample adjustment. K2017-1 reduced the absolute value of bias from 0.07 to 0.00 mm for snowfall. Finally, Z2004, M2007, and K2017-1 yielded better adjustment results for the daily accumulation precipitation amount (>2 mm d−1), individual snowfall events (>2 mm per event), and half-hourly accumulation snowfall or mixed samples (>1 mm 30 min−1), respectively. However, further intercomparison in different climate regions is needed for trace precipitation samples.

Modelling Weather Precipitation Intensity on Surfaces in Motion with Application to Autonomous Vehicles.

With advances in the development of autonomous vehicles (AVs), more attention has been paid to the effects caused by adverse weather conditions. It is well known that the performance of self-driving vehicles is reduced when they are exposed to stressors that impair visibility or cause water or snow accumulation on sensor surfaces. This paper proposes a model to quantify weather precipitation, such as rain and snow, perceived by moving vehicles based on outdoor data. The modeling covers a wide range of parameters, such as varying the wind direction and realistic particle size distributions. The model allows the calculation of precipitation intensity on inclined surfaces of different orientations and on a circular driving path. The modeling results were partially validated against direct measurements carried out using a test vehicle. The model outputs showed a strong correlation with the experimental data for both rain and snow. Mitigation strategies for heavy precipitation on vehicles can be developed, and correlations between precipitation rate and accumulation level can be traced using the presented analytical model. A dimensional analysis of the problem highlighted the critical parameters that can help the design of future experiments. The obtained results highlight the importance of the angle of the sensing surface for the perceived precipitation level. The proposed model was used to analyze optimal orientations for minimization of the precipitation flux, which can help to determine the positioning of sensors on the surface of autonomous vehicles.

Research on urban waterlogging risk prediction based on the coupling of the BP neural network and SWMM model

Abstract Scientific and effective urban waterlogging risk prediction can help improve urban waterlogging disaster prevention capabilities. Combining the numerical simulation model with the data-driven model, the construction of the urban waterlogging risk predictive model can satisfy the prediction accuracy and improve the prediction timeliness. Thus, this paper established an urban waterlogging risk predictive model based on the coupling of the BP neural network and SWMM model, and set five input patterns, finally selected the accumulative precipitation process and precipitation characteristics as input to predict the regional waterlogging risks under different urban rainstorm scenarios. The results show that the overall performance of the pipe drainage system in the study area is lower, and it cannot resist the rainstorm with a higher return period. Moreover, the total waterlogging risk of the southern old city is higher than that of the northern new city in the study area. The calculation speed of the prediction model constructed in this paper is thousands of times higher than that of the numerical model, so the calculation speed is very fast, which meets the requirements of the forecast timeliness.

Influence of the harvesting method on the processes of moisture accumulation and yield capacity

BACKGROUND: A feature of the climate of the steppe zones of Western Siberia is the frequent recurrence of droughts. Accumulation and preservation of solid precipitation in the form of snow on field in winter is a relevant task, since this directly affects the accumulation of productive moisture in soil by the time of sowing and, as a result, the formation of the future harvest.&#x0D; AIMS: To determine the influence of the snow cover level, moisture reserves and productive moisture in the soil on a stubble background, harvested with a thresher for direct combining and a rippler on the yield capacity of spring wheat.&#x0D; METHODS: The field experiment was carried out in 2021-2022 in the steppe zone of the Omsk region at the Novouralskoye Scientific and Production Enterprise of the Omsk Agrarian Scientific Center. In the autumn of 2021, sections of the field were marked out for measurements: wheat stubble field after the thresher for direct combining, wheat stubble field after the rippler. In March 2022, indicators of the height of the snow cover and moisture reserves in the snow were determined on the marked areas using the VS-43 balance snow gauge. In the spring, wheat was sown with the machine-tractor unit, consisting of the RSM 2400 tractor and the Amazone Condor 15001-C sowing facility. In autumn, the chosen field sections were harvested with the Sampo-250 seed-production harvester.&#x0D; RESULTS: It was found that after harvesting with the thresher for direct combining with a stubble background of about 15 cm, there is a decrease in the moisture reserve in the one-meter-depth layer by almost 2 times, down to 48.6 mm, by the spring period. The moisture reserve on a high stubble background, after the operation of the rippler, on the contrary, was able to persist, as there were 96.6 mm of productive moisture in the one-meter-depth layer by the time of sowing. The average grain yield on the field section sown after the rippler was 1.16 t/ha. The average grain yield on the field section sown after harvesting with the thresher for direct combining was 1.06 t/ha. Thus, the grain increase was 0.1 t/ha or 9.4%.&#x0D; CONCLUSIONS: The carried out field experiment indicates that, when harvesting with a rippler, there is a twofold increase in productive moisture by the time of sowing, while the increase in the yield of spring soft wheat grain was 9.4%.